Tomographic subvolume alignment and subvolume classification applied to myosin V and SIV envelope spikes

Electron tomography is a technique for three-dimensional reconstruction, that is widely used for imaging macromolecules, macromolecular assemblies or whole cells. Combined with cryo-electron microscopy, it is capable of visualizing structural detail in a state close to in vivo conditions in the cell. In electron tomography, micrographs are taken while tilting the specimen to different angles about a fixed axis. Due to mechanical constraints, the angular tilt range is limited. As a consequence, the reconstruction of a 3D image is missing data, which for a single axis tilt series is called the “missing wedge”, a region in reciprocal space where Fourier coefficients cannot be obtained experimentally. Tomographic data is analyzed by extracting subvolumes from the raw tomograms, by alignment of the extracted subvolumes, multivariate data analysis, classification, and class-averaging, which results in an increased signal-to-noise ratio and substantial data reduction. Subvolume analysis is a valuable tool to discriminate heterogeneous populations of macromolecules, or conformations of a macromolecule or macromolecular assembly as well as to characterize interactions between macromolecules. However, this analysis is hampered by the lack of data in the original tomograms caused by the missing wedge. Here, we report enhancements of our subvolume processing protocols in which the problem of the missing data in reciprocal space is addressed by using constrained correlation and weighted averaging in reciprocal space. These procedures are applied to the analysis of myosin V and simian immunodeficiency virus (SIV) envelope spikes. We also investigate the effect of the missing wedge on image classification and establish limits of reliability by model calculations with generated phantoms.